A multi-level screening mass spectrometry database for Ganoderma lucidum, its establishment method and application

Abstract

This invention belongs to the field of natural product detection technology, specifically relating to a multi-level screening mass spectrometry database for Ganoderma lucidum, its establishment method, and its application. The database establishment includes the following steps: (1) constructing a primary mass spectrometry database based on Ganoderma lucidum compound standards; (2) constructing a secondary mass spectrometry database based on measured mass spectrometry data of Ganoderma lucidum samples and publicly available component information of Ganoderma lucidum, using an information processing platform to simulate fragment fragmentation, predict mobility values, and perform data comparison; (3) constructing a tertiary mass spectrometry database based on an open-source database, utilizing various complex component mass spectrometry data processing strategies for traditional Chinese medicine and phytochemical taxonomy, combined with an information processing platform to simulate fragment fragmentation and predict mobility values. This invention enables qualitative screening of Ganoderma lucidum-related samples in the absence of standards, and features high throughput, accuracy, simplicity, and speed.

Description

Technical Field

[0001] This invention belongs to the field of natural product detection technology, specifically relating to a multi-level screening mass spectrometry database for Ganoderma lucidum, its establishment method, and its application. Background Technology

[0002] Reishi mushroom is the dried fruiting body of the fungus *Ganoderma lucidum* or *Ganoderma sinense*, belonging to the Polyporaceae family. It is one of my country's well-known medicinal fungi. The efficacy and clinical applications of reishi mushroom are recorded in ancient medical works such as *Shennong Bencao Jing*, *Xinxiu Bencao*, and *Bencao Gangmu*. Modern pharmacological and clinical studies have also shown that reishi mushroom has anti-tumor, immune-regulating, liver-protective, anti-aging, and cardiovascular disease-preventing effects.

[0003] Material basis research indicates that Ganoderma lucidum has a complex chemical composition, containing various active ingredients such as polysaccharides, triterpenoids, proteins, amino acids, sterols, and fatty acids. In particular, Ganoderma lucidum triterpenoids, as one of the main active components, possess a variety of activities including antitumor, immunomodulatory, antiviral, antiepileptic, anti-inflammatory, and neuroprotective effects. Currently, nearly 320 Ganoderma lucidum triterpenoids have been reported, and numerous isomers exist, making identification challenging.

[0004] Currently, the main methods for detecting components in Ganoderma lucidum are colorimetry, liquid chromatography, and liquid chromatography-mass spectrometry (LC-MS). Each of these methods has its advantages and disadvantages. Colorimetry is inexpensive but can only be used for total content determination and is subject to significant interference. Liquid chromatography is the most important detection technique, usually coupled with UV-Vis detectors for component detection, but it is limited by standards, sensitivity, matrix interference, etc., making it difficult to identify each chromatographic peak during the analysis.

[0005] Chinese invention patent application CN201210222763.1 discloses a quality analysis method for pharmaceuticals or health products, specifically relating to a quality control method for Ganoderma lucidum water extract. To specifically control product quality, the sum of crude polysaccharide content and the contents of ganoderic acids A, B, and C2 is used as the quality control index for Ganoderma lucidum water extract. This study extracts crude polysaccharides from Ganoderma lucidum using a water extraction and alcohol precipitation method, precipitates polysaccharides with dextran structures using copper reagent, and determines their content using ultraviolet spectrophotometry, eliminating the influence of excipients on the determination and providing a basis for the quality control of Ganoderma lucidum water extract. However, this method can only measure the total content of the above components and is subject to significant interference.

[0006] Chinese invention patent application CN201910560738 relates to characteristic spectra, their construction methods, and applications, particularly to the HS-SPME / GC-MS characteristic spectra of volatile components of Ganoderma lucidum, their construction methods, and applications. This application discloses HS-SPME / GC-MS characteristic spectra of volatile components of Ganoderma lucidum. By establishing characteristic spectra of Ganoderma lucidum, qualitative analysis is performed on common peaks, thereby distinguishing and identifying the Ganoderma lucidum or other edible fungi and related products. GC-MS characteristic spectra analysis can also be used to identify different varieties of Ganoderma lucidum. However, due to the complexity of Ganoderma lucidum components and the presence of numerous isomers, the detection accuracy is not yet sufficient.

[0007] With the development of mass spectrometry technology, especially high-resolution mass spectrometry, it has been widely used in the field of analysis due to its high throughput, high resolution, and library search capabilities. Combined with high throughput, accurate molecular weight, and library search functions, it can quickly identify natural products.

[0008] Current literature reports the analysis of chemical components in Ganoderma lucidum using mass spectrometry or liquid chromatography-mass spectrometry (LC-MS). However, due to the complexity of Ganoderma lucidum components, the presence of numerous isomers, and the lack of a systematic mass spectrometry database for Ganoderma lucidum chemical components, the number of directly identifiable chemical components is limited, and the accuracy is not high. Therefore, this paper presents a method for identifying secondary metabolites in Ganoderma lucidum by establishing a multi-level high-throughput screening mass spectrometry database, primarily consisting of a primary database (standard library), a secondary database (local library), and a tertiary database (online database). Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a multi-level screening mass spectrometry database for Ganoderma lucidum, its establishment method, and its application. By constructing a primary database (standard library), a secondary database (local library), and a tertiary database (online database), a multi-level mass spectrometry database is formed, capable of accurately identifying complex secondary metabolites in Ganoderma lucidum. This database is then used for high-throughput screening and identification of secondary metabolites in Ganoderma lucidum.

[0010] To achieve the above-mentioned objectives of this invention, the specific technical solution adopted by this invention is as follows:

[0011] A method for establishing a multi-level screening mass spectrometry database for Ganoderma lucidum includes the following steps:

[0012] (1) Based on Ganoderma lucidum compound standards, data were collected using different acquisition modes of a high-resolution mass spectrometer, and the data of Ganoderma lucidum compound standards were analyzed and a primary mass spectrometry database was constructed.

[0013] (2) Based on the measured mass spectrometry data of Ganoderma lucidum samples and the publicly available component information of Ganoderma lucidum, a secondary mass spectrometry database was constructed by using an information processing platform to simulate fragment fragmentation, predict mobility values ​​and compare data.

[0014] (3) Based on open-source databases, a three-level mass spectrometry database was constructed by using mass spectrometry data processing strategies for complex components of Chinese herbal medicines and phytochemical taxonomy, combined with information processing platforms to simulate fragment fragmentation and predict mobility values.

[0015] Specifically, the method for establishing a primary mass spectrometry database based on standards according to the present invention is as follows: using different types of mass spectrometers, under different acquisition modes and different mass spectrometry parameter conditions, mass spectrometry information of standards is acquired to establish a primary mass spectrometry database containing relevant information such as accurate molecular mass, structural formula, retention time, accurate molecular weight and ionic strength of primary fragments, accurate molecular weight and ionic strength of secondary fragments, ion mobility, drift time and collision cross-sectional area.

[0016] Preferably, the standards mentioned in step (1) cover representative Ganoderma lucidum triterpenoids with different skeletons consisting of 24, 27, and 30 carbons as the parent nucleus; the different modes include MS E The primary mass spectrometry database includes DDA, DIA, Full Scan, MIM-EPI, SONAR, SWATH, and HDMS; the primary mass spectrometry database includes precise molecular mass, structural formula, retention time, precise molecular weight and ionic intensity of primary fragments, precise molecular weight and ionic intensity of secondary fragments, ion mobility, drift time, and collision cross-section.

[0017] Preferably, the standards mentioned in step (1) cover representative Ganoderma lucidum triterpenoids with different skeletons consisting of 24, 27, and 30 carbons as parent nuclei. Accurate retention times, precise molecular weights and ionic intensities of primary and secondary fragments, ion mobility, drift time, and collision cross-sectional area are collected via mass spectrometry. Simultaneously, the mass spectrometry fragmentation patterns of different parent nucleus skeletons are summarized. The different modes include MS. E , DDA, DIA, Full Scan, MIM-EPI, SONAR, SWATH and HDMS.

[0018] Preferably, the primary database in step (1) includes relevant information such as chemical formula, structural formula, retention time, precise molecular weight and ionic strength of primary fragments, precise molecular weight and ionic strength of secondary fragments, ionic mobility, drift time, and collision cross-sectional area.

[0019] The primary database in this invention has the following characteristics: (1) It has a complete range of scanning modes, basically covering all types of mass spectrometers and their scanning modes, including MS. E(1) DDA, DIA, Full Scan, MIM-EPI, SONAR, SWATH and HDMS; (2) The number and types of standards are complete, covering representative Ganoderma triterpenoids with 24 carbon, 27 carbon and 30 carbon as the parent skeleton. Compounds with the same parent skeleton have similar fragmentation rules. Collecting mass spectrometry information of representative compounds helps to summarize the mass spectrometry fragmentation rules of different types of parent skeletons. This mass spectrometry fragmentation rule is of great significance when identifying unknown compounds without standards, and can help identify the basic parent skeleton of unknown compounds; (3) The compound information is accurate, such as accurate retention time, accurate molecular weight and ionic strength of primary and secondary fragments, ion mobility drift time and collision cross-section, etc. Among them, ion mobility drift time and collision cross-section are closely related to the mass-to-charge ratio of ions, the number of ions with charge and the three-dimensional structure of ions. This is of great significance for identifying isomers and compounds with similar structures. At the same time, it realizes separation on the basis of retention time and mass-to-charge ratio, making the identification results more accurate.

[0020] Preferably, the information processing platform in step (2) includes at least one of Peakview software, UNIFI software, Masslynx software, Progenesis QI software, MassFrontier software, Compound Discoverer software, TraceFinder software, MassHunter software, Skyline software, MS-DIAL software, and AllCCS platform; the UNIFI software performs simulated fragment fragmentation matching, and the AllCCS platform performs mobility value prediction.

[0021] Specifically, the method for establishing a secondary database based on reported components of the Ganoderma genus according to the present invention is as follows: mass spectrometry data of Ganoderma genus samples are collected, Ganoderma-related components are selected to construct an initial dataset, and based on the initial dataset, information processing platforms such as UNIFI, GNPS and SIRIUS 4 are used to perform dual precise simulations through simulated fragment fragmentation and mobility value prediction to construct a secondary mass spectrometry database capable of accurately identifying compound structures.

[0022] Preferably, the sources of information on reported components of the Ganoderma genus in step (2) include publicly available literature and large chemical databases (Reaxys, Pubchem, Scifinder, TCM, etc.).

[0023] Preferably, the information processing platform in step (2) includes Peakview software, UNIFI software, Masslynx software, Progenesis QI software, MassFrontier software, Compound Discoverer software, TraceFinder software, MassHunter software, Skyline software, MS-DIAL software, and the AllCCS platform.

[0024] More preferably, UNIFI software is selected for simulated fragment fragmentation matching and AllCCS platform for mobility value prediction.

[0025] The secondary mass spectrometry database in this invention has the following characteristics: (1) It has a wide coverage, covering all reported components of the Ganoderma genus, including literature data and large chemical databases such as Reaxys, SciFinder, and PubChem, as well as all information on chemical components of the Ganoderma genus; (2) It has high accuracy, using multiple platforms such as UNIFI, GNPS, SIRIUS 4, and AllCCS to perform precise fragment simulation and mobility value prediction of compounds to obtain accurate compound identification information.

[0026] Specifically, the method for establishing a three-level database based on a real-time updated open-source database described in this invention is as follows: using various mass spectrometry data processing strategies for complex components of traditional Chinese medicine, as well as phytochemical taxonomy, and combining information platforms such as UNIFI, GNPS, and SIRIUS 4, simulated fragment fragmentation and mobility value prediction are performed for accurate confirmation, thereby constructing a three-level mass spectrometry database capable of accurately identifying compound structures.

[0027] Preferably, the open-source database mentioned in step (3) includes more than 350 open-source databases such as ChemSpider, COCONUT, Super Natural II, NPASS, Massbank, KEGG, and FooDB. This open-source database has the following characteristics: (1) wide coverage, covering all known chemical components, including synthetic and natural products. (2) real-time, with regular updates to the data in the database to ensure the accuracy and timeliness of the information.

[0028] Preferably, the mass spectrometry data processing strategy for complex components of traditional Chinese medicine in step (3) includes at least one of the following: mass spectrometry dendrogram similarity filtering technology based on template compounds, "fragment tree" strategy for de novo identification of unknown compounds based on fragment fingerprint features, molecular network based on secondary fragment similarity score, and compound prediction strategy based on molecular descriptor; the phytochemical taxonomy includes Ganoderma chemical taxonomy based on Ganoderma related biosynthetic pathways and / or known chemical structure nuclei based on the chemical components of Ganoderma.

[0029] The three-level mass spectrometry database in this invention has the following characteristics: (1) Real-time, the relevant compound information in the database can be updated and expanded in real time; (2) Accuracy, based on the mass spectrometry data processing strategy of complex components of traditional Chinese medicine and phytochemical taxonomy, combined with scientific information system to simulate fragment fragmentation and mobility value prediction, accurate compound identification information can be obtained.

[0030] The present invention also relates to a database established by the above-described method, wherein the database is updated periodically or in real time.

[0031] This invention also relates to the application of the database established by the above-described method in the identification of secondary metabolites in Ganoderma lucidum.

[0032] Preferably, the step of identifying secondary metabolites in Ganoderma lucidum includes:

[0033] (1) Preparation of test solution and data collection;

[0034] (2) The mass spectrometry data of the sample to be identified is compared and identified with the primary mass spectrometry database, the secondary mass spectrometry database and the tertiary mass spectrometry database using the information processing platform.

[0035] Preferably, the preparation method of the test solution in step (1) includes: taking a Ganoderma lucidum sample, adding solvent for ultrasonic extraction, filtering, and obtaining the solution; the solvent is selected from water or 70-100% methanol, preferably 100% methanol; the ultrasonic extraction time is 20-60 min, and the filtration is filtration through a 0.22-0.5 μm microporous membrane.

[0036] Specifically, the data acquisition methods for the test solution described in this invention are as follows: direct injection mass spectrometer, liquid chromatography-mass spectrometry, desorption electrospray ionization mass spectrometer (DESI-MS), direct real-time analysis mass spectrometer (DART-MS), and matrix-assisted laser desorption / ionization mass spectrometer (MALDI-MS).

[0037] Preferably, the data acquisition equipment in step (1) is selected from one or more of the following: mass spectrometer, liquid chromatography-mass spectrometry (LC-MS), desorption electrospray ionization mass spectrometer (DESI-MS), direct real-time analysis mass spectrometer (DART-MS), and matrix-assisted laser desorption / ionization mass spectrometer (MALDI-MS), preferably a liquid chromatography-mass spectrometry (LC-MS); the ion source used in the LC-MS is selected from one of ESI, ESCI, APCI, EI, and CI, preferably ESI.

[0038] More preferably, data acquisition uses a liquid chromatography-mass spectrometry (LC-MS) instrument or a direct injection mass spectrometer; more preferably, it uses a liquid chromatography-mass spectrometry (LC-MS) instrument.

[0039] In this invention, considering that secondary metabolites in the Ganoderma genus are mostly homogeneous compounds with numerous isomers, the influence of different mass spectrometry data acquisition methods, such as direct injection into the mass spectrometer, liquid chromatography-mass spectrometry (LC-MS), and DESI-MS, on the identification results was investigated. The results showed that LC-MS can obtain better peak capacity and also provides better separation of isomers of homogeneous compounds in the Ganoderma genus.

[0040] Preferably, the data acquisition mode in step (1) is selected from DDA, DIA, Full Scan, MS. E One or more of MIM-EPI, SONAR, SWATH and HDMS; preferably, SONAR and HDMS.

[0041] In this invention, DDA, DIA, Full Scan, and MS are analyzed. E The study compared various mass spectrometry acquisition methods, including MIM-EPI, SONAR, SWATH, and HDMS. Results showed that the SONAR+HDMS mode significantly reduced spectral complexity and improved the signal-to-noise ratio while assigning high-energy fragments. Furthermore, it added a one-dimensional separation (mobility value) dimension compared to traditional acquisition methods, providing an additional dimension for compound confirmation and further improving the accuracy of identification.

[0042] Specifically, the method for comparison and identification with the primary mass spectrometry database described in step (2) is as follows: The collected information is imported into an information processing platform and matched with the primary mass spectrometry database. Qualitative analysis confirmation criteria: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; fragment ion mass number error less than 3.5 ppm and at least 6 matching fragments; mobility value deviation less than 5%. If the comparison result between the screened component and a component in the primary database simultaneously meets all qualitative analysis confirmation criteria, it can be considered as that component.

[0043] Specifically, the method for comparison and identification with the secondary mass spectrometry database described in step (2) is as follows: The collected information is imported into an information processing platform. Through dual precise simulation matching with the secondary mass spectrometry database, including simulated fragment fragmentation and mobility value prediction, the corresponding matching degree (0-50, with higher matching degree indicating higher prediction accuracy) is obtained. Qualitative analysis confirmation criteria: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; fragment ion mass number error less than 3.5 ppm and at least 4 matched fragments; mobility value deviation less than 5%; matching degree ≥30. If the comparison result between the screened component and a component in the primary database simultaneously meets all qualitative analysis confirmation criteria, it can be considered as that component.

[0044] Specifically, the method for comparison and identification with the tertiary mass spectrometry database described in step (2) is as follows: The collected information is imported into the information processing platform, and the corresponding matching degree is obtained through dual precise simulation matching with the tertiary mass spectrometry database, which involves simulated fragment fragmentation and prediction of mobility values. Simultaneously, the qualitative analysis confirmation criteria are: the compound exists in the Ganoderma genus; the mass number error of the parent ion single isotope is less than 4.5 ppm; the relative abundance deviation of the isotope is less than 5%; the mass number error of the fragment ion is less than 4.5 ppm and the number of matched fragments is at least 3; the mobility value deviation is less than 8%; the oil-water partition coefficient is close to the chromatographic peak time of the component; and the matching degree is ≥20. If the matching result of the screened component with a component in the tertiary database meets all the qualitative analysis confirmation criteria, it can be considered as that component.

[0045] Preferably, the information processing platform in step (2) is selected from one or more of Peakview software, UNIFI software, Masslynx software, Progenesis QI software, MassFrontier software, Compound Discoverer software, TraceFinder software, MassHunter software, Skyline software, MS-DIAL software, and the AllCCS platform. Preferably, UNIFI software is selected for simulated fragment fragmentation matching, and the AllCCS platform is selected for mobility value prediction.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The Ganoderma lucidum database constructed in a multi-level manner provided by the present invention can realize the qualitative screening of Ganoderma lucidum related samples in the absence of standard products. It has the characteristics of high throughput, accuracy, simplicity and speed. At the same time, it solves the problems of limited standard products, large workload and untimely database updates in the traditional identification process, and provides technical support for the research on the material basis of Ganoderma lucidum and the identification of medicinal substances.

[0048] (2) The multi-level database constructed in this invention adds a one-dimensional (ion mobility) separation to the traditional method, thereby providing an additional dimension (mobility value) for compound confirmation and further improving the accuracy of identification. In addition, it combines many information processing platforms such as the UNIFI scientific information system and molecular network platform to simulate and match its fragments and mobility values, which greatly improves the accuracy and reliability of compound identification;

[0049] (3) The three-level database constructed in this invention uses a real-time updated open-source database while combining various mass spectrometry data processing strategies for complex components of traditional Chinese medicine, such as molecular networks (MN) based on secondary fragment similarity scores and compound prediction strategies based on molecular descriptors, as well as phytochemical taxonomy and information processing platforms such as UNIFI, GNPS and SIRIUS 4. This avoids the errors caused by isomers and open-source databases due to the inclusion of too many compounds, and further improves the accuracy and reliability of compound identification. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the establishment and application of a multi-level high-throughput screening mass spectrometry database for Ganoderma lucidum.

[0051] Figure 2 This is a schematic diagram illustrating the process of establishing and comparing a primary database.

[0052] Figure 3 and Figure 4 This is a schematic diagram illustrating the process of establishing and comparing the secondary database;

[0053] Figure 5 This is a schematic diagram of the three-level database establishment and comparison process;

[0054] Figure 6 and Figure 7 This is a visualization of the GNPS information processing platform identification results;

[0055] Figure 8 This is a principal component analysis (PCA) diagram of different Ganoderma species based on the identification results of a multi-level screening database, where BR—Ganoderma lucidum; CC—Ganoderma umbellata; CZ—Ganoderma rubrum; GT—Ganoderma pine; GW—Ganoderma wormwood; NF—Ganoderma serrata; SS—Ganoderma grosvenorii; WB—Ganoderma sessileum. Detailed Implementation

[0056] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further explained below in conjunction with the accompanying drawings and embodiments. However, the scope of protection of the present invention is not limited in any way by the embodiments.

[0057] Example 1: Establishment of a multi-level high-throughput screening mass spectrometry database for Ganoderma lucidum

[0058] like Figure 1 As shown, the specific process for establishing the Ganoderma lucidum multi-level high-throughput screening mass spectrometry database of the present invention is as follows:

[0059] (1) Establishment of the primary database

[0060] Standards of compounds contained in Ganoderma lucidum (including purity and NMR identification certificates) were collected. The names and CAS numbers of the obtained compounds are detailed in Table 1.

[0061] Weigh 5 mg of each Ganoderma lucidum compound standard, dilute with methanol to 50 mL, mix well, and prepare a single standard solution with a concentration of 0.1 mg / mL. Inject into a Synapt XS high-resolution liquid chromatography-mass spectrometry (LC-MS) system. Instrument parameters are as follows:

[0062] The chromatographic column was a C18 UPLC column (2.1 mm × 100 mm, 1.8 μm); the mobile phase was 0.1% (v / v) formic acid in water (A) - acetonitrile (B). Gradient elution was performed at a flow rate of 0.3 mL / min: 0–9 min, 20–28% B; 9–28 min, 28–60% B; 28–45 min, 60–100% B. -1 The column temperature was 30℃, and the injection volume was 1 μL. Electrospray ionization (ESI) was used in negative ion mode with a mass scan range of 100-1500 Da, an ion source temperature of 150℃, a capillary voltage of 3 kV, an orifice voltage of 40 V, a collision energy of 20-50 eV, a nebulizer gas pressure of 6.0 bar, a nebulizer gas temperature of 500℃, and a nebulizer gas flow rate of 1000 L·h. -1 Leucine-enkephalin (ESI-: 554.2615 Da) solution was used as a calibration solution with a concentration of 200 pg·mL. -1 .

[0063] This system records information such as retention time of compounds, precise molecular weight and ionic strength of primary and secondary fragments, mobility drift time, and collision cross-sectional area. It also summarizes the mass spectrometry fragmentation patterns of different parent skeletons. Based on this, a primary database mainly composed of Ganoderma lucidum compound standards is established, such as... Figure 2 .

[0064] Table 1. Ganoderma compounds currently covered in the primary database.

[0065]

[0066]

[0067] (2) Establishment of a secondary database

[0068] Three samples of each of the following Ganoderma genera were collected: Ganoderma applanatum, Ganoderma australe, Ganoderma boninense, Ganoderma gibbosum, Ganoderma leucocontextum, Ganoderma multipileum, Ganoderma resinaceum, Ganoderma lucidum, Ganoderma sinense, Ganoderma tsugqe, and Ganoderma weberianum. 0.1 g of each sample was weighed and placed in a 50 mL centrifuge tube. 40 mL of methanol was accurately added, and the mixture was extracted by sonication for 30 min. 1 mL of the supernatant was transferred to a 1.5 mL centrifuge tube and centrifuged at 13000 rpm for 10 min. The supernatant was then injected into a Synapt XS high-resolution liquid chromatography-mass spectrometry (LC-MS) system. The instrument parameters are as follows:

[0069] The chromatographic column was a C18 UPLC column (2.1 mm × 100 mm, 1.8 μm); the mobile phase was 0.1% (v / v) formic acid in water (A) - acetonitrile (B). Gradient elution was performed at a flow rate of 0.3 mL / min: 0–9 min, 20–28% B; 9–28 min, 28–60% B; 28–45 min, 60–100% B. -1 The column temperature was 30℃, and the injection volume was 1 μL. Electrospray ionization (ESI) was used in negative ion mode with a mass scan range of 100-1500 Da, an ion source temperature of 150℃, a capillary voltage of 3 kV, an orifice voltage of 40 V, a collision energy of 20-50 eV, a nebulizer gas pressure of 6.0 bar, a nebulizer gas temperature of 500℃, and a nebulizer gas flow rate of 1000 L·h. -1 Leucine-enkephalin (ESI-: 554.2615 Da) solution was used as a calibration solution with a concentration of 200 pg·mL. -1 .

[0070] Collect sample data and record information such as retention time, precise molecular weight, ionic strength, mobility drift time, and collision cross-sectional area of ​​all primary and secondary ions.

[0071] An initial dataset was formed by collecting data from all relevant literature and large chemical databases covering all reported compounds in the Ganoderma genus. This dataset includes information such as the Ganoderma source attribution, name, molecular formula, structural formula, secondary fragments, and oil-water partition coefficient. Using information processing platforms such as UNIFI, GNPS, and SIRIUS 4, a double-precise simulation comparison was performed between simulated fragment fragmentation and mobility value predictions and all components in the collected Ganoderma samples. For components meeting the following criteria: single isotope mass number error of the parent ion less than 3.5 ppm; relative isotope abundance deviation less than 5%; fragment ion mass number error less than 3.5 ppm with at least 4 matching fragments; mobility value deviation less than 5%; oil-water partition coefficient close to the chromatographic peak time of the component; and matching degree ≥30, qualitative identification of the component can be completed. Based on this qualitative analysis, a secondary mass spectrometry database capable of accurately identifying compound structures and retention times was formed. Figure 3 and Figure 4 .

[0072] (3) Establishment of a three-level database

[0073] Numerous real-time updated open-source databases, such as ChemSpider, MassBank, and KEGG, combined with platforms like UNIFI, GNPS, and SIRIUS 4, were used to simulate fragment fragmentation and predict mobility values. These simulations were then compared with unidentified components in primary and secondary mass spectrometry databases of Ganoderma lucidum samples to obtain the corresponding matching degree. For compounds meeting the following criteria: they exist within the Ganoderma genus; the single isotope mass number error of the parent ion is less than 3.5 ppm; the relative abundance deviation of the isotope is less than 5%; the fragment ion mass number error is less than 3.5 ppm and the number of matching fragments is at least 3; the mobility value deviation is less than 8%; the oil-water partition coefficient is close to the chromatographic peak time of the component; the matching degree is ≥20; and the compound type is found in the Ganoderma genus, the component can be qualitatively identified. Based on this qualitative analysis, a tertiary mass spectrometry database capable of accurately identifying compound structures is formed, such as... Figure 5 .

[0074] Example 2: Identification of Ganoderma lucidum secondary metabolites based on a multi-level mass spectrometry database

[0075] (1) Preparation of test samples

[0076] Accurately weigh 0.1g of Ganoderma lucidum sample powder and place it in a 50mL centrifuge tube. Accurately add 40mL of methanol and extract by sonication for 30min. Take 1mL of the supernatant and place it in a 1.5mL centrifuge tube. Centrifuge at 13000r / min for 10min and use the supernatant as the sample to be tested.

[0077] (2) Acquisition of primary and secondary mass spectra of the sample to be tested

[0078] The samples to be tested were analyzed using a Synapt XS high-resolution liquid chromatography-mass spectrometry (LC-MS) system. The instrument parameters are as follows:

[0079] Chromatographic conditions: The column was a C18 UPLC column (2.1 mm × 100 mm, 1.8 μm); the mobile phase was 0.1% (v / v) formic acid in water (A) - acetonitrile (B). Gradient elution was performed at a flow rate of 0.3 mL / min: 0–9 min, 20–28% B; 9–28 min, 28–60% B; 28–45 min, 60–100% B. -1 The column temperature was 30℃ and the injection volume was 1μL.

[0080] Mass spectrometry conditions: Analysis was performed using an electrospray ionization source in negative ion mode, with a mass scan range of 100-1500 Da, an ion source temperature of 150 °C, a capillary voltage of 3 kV, an orifice voltage of 40 V, a collision energy of 20-50 eV, a nebulizer gas pressure of 6.0 bar, a nebulizer gas temperature of 500 °C, and a nebulizer gas flow rate of 1000 L·h. -1 Leucine-Enkephalin (ESI) - The 554.2615 Da solution was used as a correction solution with a concentration of 200 pg·mL. -1 .

[0081] (3) Qualitative analysis of components

[0082] The collected information was imported into scientific information systems such as UNIFI. First, it was matched against a primary database, and qualitative analysis was performed to confirm the following criteria: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; fragment ion mass number error less than 3.5 ppm with at least 6 matched fragments; and mobility value deviation less than 5%. Based on the primary database, 29 components were identified.

[0083] Secondly, a dual-precise simulation comparison was performed using a secondary database to simulate fragment fragmentation and predict mobility values, yielding the corresponding matching degree. Qualitative analysis confirmation criteria were: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative isotope abundance deviation less than 5%; fragment ion mass number error less than 3.5 ppm with at least 4 matching fragments; mobility value deviation less than 5%; and matching degree ≥30. A total of 142 components were identified based on the secondary database.

[0084] Finally, further comparisons were performed using a three-tiered database to obtain the corresponding matching degree. Qualitative analysis confirmation criteria: the compound type exists within the *Ganoderma* genus; the single isotope mass number error of the parent ion is less than 4.5 ppm; the relative abundance deviation of the isotope is less than 5%; the fragment ion mass number error is less than 4.5 ppm and the number of matching fragments is at least 4; the mobility value deviation is less than 8%; the oil-water partition coefficient is close to the peak time of this component; and the matching degree is ≥20. A total of 33 components were identified based on the three-tiered database.

[0085] Finally, through the multi-level Ganoderma lucidum database retrieval, a total of 204 components were identified from the Ganoderma lucidum samples. The specific identification information is shown in Table 2 below. This embodiment has the following advantages over existing methods: (1) The identification results are accurate. Compared with the two-dimensional separation identification method that separates retention time and primary and secondary fragment information in traditional identification, the addition of the mobility value dimension makes the identification results more accurate. (2) The identification efficiency is high. Traditional methods are based on manual verification, and the entire identification cycle is long. It takes more than two months to identify 100 compounds. However, through this embodiment, the database retrieval and identification are automated by software, and more than 200 components can be identified from the Ganoderma lucidum samples at one time (within 30 minutes), which significantly improves the analysis efficiency. (3) The technical threshold for identifying compounds is low. Traditional methods require personnel to have strong analytical chemistry, phytochemistry and mass spectrometry knowledge to accurately determine the identification results. However, through this embodiment, the data retrieval and determination are automated by software, which has low requirements for the technical ability of the operators.

[0086] Table 2. Identification results of secondary metabolites of Ganoderma lucidum (Red Ganoderma) based on multi-level mass spectrometry database.

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Example 3: Identification of Secondary Metabolites of Different Ganoderma Lucidum Varieties Based on Multilevel Mass Spectrometry Database

[0097] (1) Material preparation

[0098] Accurately weigh 0.1g of powder from different varieties of Ganoderma (Ganoderma applanatum, Ganoderma australe, Ganoderma leucocontextum, Ganoderma multipileum, Ganoderma resinaceum, Ganoderma lucidum, Ganoderma sinense, Ganoderma tsugqe, and Ganoderma weberianum), place them in 50mL centrifuge tubes, accurately add 40mL of methanol, and extract by sonication for 30min. Take 1mL of the supernatant and put it into a 1.5mL centrifuge tube, centrifuge at 13000r / min for 10min, and use the supernatant as the sample to be tested.

[0099] (2) Acquisition of primary and secondary mass spectra of the sample to be tested

[0100] The samples to be tested were analyzed using a Synapt XS liquid chromatography-mass spectrometry (LC-MS) system. The instrument parameters are as follows:

[0101] Chromatographic conditions: The column was a C18 UPLC column (2.1 mm × 100 mm, 1.8 μm); the mobile phase was 0.1% (v / v) formic acid in water (A) - acetonitrile (B). Gradient elution was performed at a flow rate of 0.3 mL / min: 0–9 min, 20–28% B; 9–28 min, 28–60% B; 28–45 min, 60–100% B. -1 The column temperature was 30℃ and the injection volume was 1μL.

[0102] Mass spectrometry conditions: Analysis was performed using an electrospray ionization source in negative ion mode, with a mass scan range of 100-1500 Da, an ion source temperature of 150 °C, a capillary voltage of 3 kV, an orifice voltage of 40 V, a collision energy of 20-50 eV, a nebulizer gas pressure of 6.0 bar, a nebulizer gas temperature of 500 °C, and a nebulizer gas flow rate of 1000 L·h. -1 Leucine-Enkephalin (ESI) - The 554.2615 Da solution was used as a correction solution with a concentration of 200 pg·mL. -1 .

[0103] (3) Qualitative analysis of components

[0104] The collected information was imported into scientific information systems such as UNIFI. First, it was matched through a primary database, and qualitative analysis was performed to confirm the criteria: retention time deviation within ±0.15 min; mass number error of a single isotope of the parent ion less than 3.5 ppm; mass number error of fragment ions less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; mobility value deviation less than 5%; and at least 6 matched fragments.

[0105] Secondly, a dual-precise simulation comparison was performed using a secondary database to simulate fragment fragmentation and predict mobility values, yielding the corresponding matching degree. Simultaneously, the GNPS information processing platform was used to further simulate fragment fragmentation of unidentified components and compare it with the initial dataset in the secondary database. The visualization results are shown below. Figure 6 and Figure 7 The identified components are then entered into a secondary database. Qualitative analysis confirmation criteria: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; fragment ion mass number error less than 3.5 ppm and at least 4 matching fragments; mobility value deviation less than 5%; matching degree ≥30.

[0106] Finally, further comparisons were conducted using a three-tiered database. Qualitative analysis confirmed the following criteria: the compound belongs to the Ganoderma genus; the single isotope mass number error of the parent ion is less than 4.5 ppm; the relative abundance deviation of the isotope is less than 5%; the fragment ion mass number error is less than 4.5 ppm; the number of matching fragments is at least 3; the mobility value deviation is less than 8%; the matching degree is ≥20; the oil-water partition coefficient is close to the peak time of this component, and this compound type exists within the Ganoderma genus.

[0107] A multi-level database of Ganoderma lucidum was constructed, and 408 secondary metabolites were identified from samples of different Ganoderma species (Table 3). Multivariate statistical analysis was then performed on the identified components. Figure 8 The study found that different Ganoderma species samples could be distinguished based on identified compounds. This example shows that the method has the following advantages over traditional methods: (1) high identification efficiency, which can automatically identify multiple sets of data in a short time and identify more components; (2) wide application range, which can identify other Ganoderma species samples in addition to identifying the components of the source (Ganoderma lucidum and Ganoderma sinense) specified under the Ganoderma section of the Chinese Pharmacopoeia.

[0108] Table 3. Identification results and intensity values ​​of secondary metabolites of different Ganoderma lucidum varieties based on multi-level mass spectrometry database.

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[0126] The above detailed description is a specific description of one of the feasible embodiments of the present invention. This embodiment is not intended to limit the patent scope of the present invention. All equivalent implementations or modifications that do not depart from the present invention should be included within the scope of the technical solution of the present invention.

Claims

1. A method for establishing a multi-level screening mass spectrometry database for Ganoderma lucidum, characterized in that, The steps are as follows: (1) Based on Ganoderma lucidum compound standards, data were collected using different acquisition modes of a high-resolution mass spectrometer, the data of Ganoderma lucidum compound standards were analyzed, and a primary mass spectrometry database was constructed. (2) Based on the measured mass spectrometry data of Ganoderma and the publicly available component information of Ganoderma, a secondary mass spectrometry database was constructed by using an information processing platform to simulate fragment fragmentation, predict mobility values ​​and compare data. (3) Based on open-source databases, a three-level mass spectrometry database was constructed by using mass spectrometry data processing strategies for complex components of traditional Chinese medicine and phytochemical taxonomy, combined with information processing platform to simulate fragment fragmentation and predict mobility values. The standard mentioned in step (1) covers representative Ganoderma triterpenoid components with different skeletons consisting of 24 carbon, 27 carbon and 30 carbons.

2. The method for establishing according to claim 1, characterized in that, The different acquisition modes mentioned in step (1) include MS E The primary mass spectrometry database includes DDA, DIA, Full Scan, MIM-EPI, SONAR, SWATH, and HDMS; the primary mass spectrometry database includes precise molecular mass, structural formula, retention time, precise molecular weight and ionic intensity of primary fragments, precise molecular weight and ionic intensity of secondary fragments, ion mobility, drift time, and collision cross-section.

3. The method for establishing according to claim 1, characterized in that, The information processing platform mentioned in step (2) includes at least one of Peakview software, UNIFI software, Masslynx software, Progenesis QI software, MassFrontier software, Compound Discoverer software, TraceFinder software, MassHunter software, Skyline software, MS-DIAL software, and AllCCS platform; the UNIFI software performs simulated fragment fragmentation matching, and the AllCCS platform performs mobility value prediction.

4. The method for establishing according to claim 1, characterized in that, The mass spectrometry data processing strategy for complex components of traditional Chinese medicine mentioned in step (3) includes at least one of the following: mass spectrometry dendrogram similarity filtering technology based on template compounds, "fragment tree" strategy for de novo identification of unknown compounds based on fragment fingerprint features, molecular network based on secondary fragment similarity score, and compound prediction strategy based on molecular descriptor; the phytochemical taxonomy includes Ganoderma chemical taxonomy based on Ganoderma related biosynthetic pathways and / or known chemical structure nuclei based on the chemical components of Ganoderma.

5. A database established by the method according to any one of claims 1-4, characterized in that, The database is updated periodically or in real time.

6. The application of a database established by the method according to any one of claims 1-4 in the identification of secondary metabolites in Ganoderma lucidum.

7. The application according to claim 6, characterized in that, The steps for identifying secondary metabolites in Ganoderma lucidum include: (1) Preparation of test solution and data collection; (2) The mass spectrometry data of the sample to be identified is compared and identified with the primary mass spectrometry database, the secondary mass spectrometry database and the tertiary mass spectrometry database using the information processing platform.

8. The application according to claim 7, characterized in that, The preparation method of the test solution in step (1) includes: taking a Ganoderma lucidum sample, adding solvent for ultrasonic extraction, filtering, and obtaining the solution; the solvent is selected from water or 70-100% methanol; the ultrasonic extraction time is 20-60 min.

9. The application according to claim 7, characterized in that, The data acquisition equipment in step (1) is selected from one or more of mass spectrometer, liquid chromatography-mass spectrometry (LC-MS), DESI-MS, DART-MS, and MALDI-MS; the ion source used in the LC-MS is selected from one of ESI, ESCI, APCI, EI, and CI; and the data acquisition mode is selected from DDA, DIA, Full Scan, and MS. E One or more of MIM-EPI, SONAR, SWATH, and HDMS.

10. The application according to claim 7, characterized in that, The confirmation criteria for comparative identification mentioned in step (2) include: S1: Qualitative analysis confirmation criteria for the primary mass spectrometry database: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; fragment ion mass number error less than 3.5 ppm and at least 6 matching fragments; mobility value deviation less than 5%; if the comparison result between the screened component and a component in the primary database simultaneously meets all the qualitative analysis confirmation criteria, it is considered to be that component. S2: Qualitative analysis confirmation criteria for the secondary mass spectrometry database: retention time deviation within ±0.15 min; single isotope mass number error of the parent ion less than 3.5 ppm; relative abundance deviation of isotopes less than 5%; fragment ion mass number error less than 3.5 ppm and at least 4 matching fragments; mobility value deviation less than 5%; matching degree ≥30; the matching degree is obtained through dual precise simulation matching with the secondary mass spectrometry database using simulated fragment fragmentation and mobility value prediction; if the comparison result between the screened component and a component in the secondary database simultaneously meets all the qualitative analysis confirmation criteria, it is considered to be that component; S3: Qualitative analysis confirmation criteria for the Level 3 mass spectrometry database: The mass number error of the parent ion single isotope is less than 4.5 ppm; the relative abundance deviation of the isotope is less than 5%; the mass number error of the fragment ion is less than 4.5 ppm and the number of matching fragments is at least 3; the mobility value deviation is less than 8%; the matching degree is ≥20; if the comparison result between the screened component and a component in the Level 3 database simultaneously meets all the qualitative analysis confirmation criteria, it is considered to be that component.

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